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2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Akihisa Hata ◽  
Noboru Fujitani ◽  
Fumiko Ono ◽  
Yasuhiro Yoshikawa

AbstractThere is a lack of an established antimicrobial resistance (AMR) surveillance system in animal welfare centers. Therefore, the AMR prevalence in shelter dogs is rarely known. Herein, we conducted a survey in animal shelters in Chiba and Kanagawa prefectures, in the Kanto Region, Japan, to ascertain the AMR status of Escherichia coli  (E. coli) prevalent in shelter dogs. E. coli was detected in the fecal samples of all 61 and 77 shelter dogs tested in Chiba and Kanagawa, respectively. The AMR was tested against 20 antibiotics. E. coli isolates derived from 16.4% and 26.0% of samples from Chiba and Kanagawa exhibited resistance to at least one antibiotic, respectively. E. coli in samples from Chiba and Kanagawa prefectures were commonly resistant to ampicillin, piperacillin, streptomycin, kanamycin, tetracycline, and nalidixic acid; that from the Kanagawa Prefecture to cefazolin, cefotaxime, aztreonam, ciprofloxacin, and levofloxacin and that from Chiba Prefecture to chloramphenicol and imipenem. Multidrug-resistant bacteria were detected in 18 dogs from both regions; β-lactamase genes (blaTEM, blaDHA-1, blaCTX-M-9 group CTX-M-14), quinolone-resistance protein genes (qnrB and qnrS), and mutations in quinolone-resistance-determining regions (gyrA and parC) were detected. These results could partially represent the AMR data in shelter dogs in the Kanto Region of Japan.


2021 ◽  
Vol 12 ◽  
Author(s):  
Naoshi Ando ◽  
Tsuyoshi Sekizuka ◽  
Eiji Yokoyama ◽  
Yoshiyuki Aihara ◽  
Noriko Konishi ◽  
...  

In Japan’s Kanto region, the number of Salmonella enterica serovar Chester infections increased temporarily between 2014 and 2016. Concurrently with this temporal increase in the Kanto region, S. Chester isolates belonging to one clonal group were causing repetitive outbreaks in Europe. A recent study reported that the European outbreaks were associated with travelers who had been exposed to contaminated food in Morocco, possibly seafood. Because Japan imports a large amount of seafood from Morocco, we aimed to establish whether the temporal increase in S. Chester infections in the Kanto region was associated with imported Moroccan seafood. Short sequence reads from the whole-genome sequencing of 47 S. Chester isolates from people in the Kanto region (2014–2016), and the additional genome sequences from 58 isolates from the European outbreaks, were analyzed. The reads were compared with the complete genome sequence from a S. Chester reference strain, and 347 single nucleotide polymorphisms (SNPs) were identified. These SNPs were used in this study. Cluster and Bayesian cluster analyses showed that the Japanese and European isolates fell into two different clusters. Therefore, ΦPT and IAS values were calculated to evaluate genetic differences between these clusters. The results revealed that the Japanese and European isolates were genetically distinct populations. Our root-to-tip analysis showed that the Japanese isolates originating from one clone had accumulated mutations, suggesting that an emergence of this organism occurred. A minimum spanning tree analysis demonstrated no correlation between genetic and geographical distances in the Japanese isolates, suggesting that the emergence of the serovar in the Kanto region did not involve person-to-person contact; rather, it occurred through food consumption. The dN/dS ratio indicated that the Japanese strain has evolved under positive selection pressure. Generally, a population of bacterial clones in a reservoir faces negative selection pressure. Therefore, the Japanese strain must have existed outside of any reservoir during its emergence. In conclusion, S. Chester isolates originating from one clone probably emerged in the Kanto region via the consumption of contaminated foods other than imported Moroccan seafood. The emerging strain may have not established a reservoir for survival in the food supply chain resulting in its disappearance after 2017.


2021 ◽  
Author(s):  
Daijiro Miyake ◽  
Daiki Hiramori

In Western countries, studies using representative surveys and community surveys have begun to reveal the size and the diversity of the asexual population. On the other hand, in Japan, there are only a few studies using representative surveys, and the detailed realities of the asexual population are yet to be explored. This article analyzed a web survey “Aromantic/Asexual Spectrum Survey 2020,” conducted by the Aro/Ace Survey Executive Committee. Most of the respondents tended to be cisgender women, young people, and residents of the southern Kanto region. Many identified as aromantic and asexual, but some identified as other aro/ace identities. We also conducted an analysis on “nonsexual,” an identity category unique to Japan. While masturbation and sex drives were found in a certain number of asexual respondents, the proportion of those who would like to have sexual contact with others was particularly low among asexual respondents. We conclude that sexual contact with others has important implications for self-identification.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mayumi Hori ◽  
Katsumi Shozugawa ◽  
Kenji Sugimori ◽  
Yuichiro Watanabe

AbstractWe conducted a comprehensive overall tap water hardness assessment for Japan. Tap water was collected from 665 points throughout Japan, and its standing position was quantitatively clarified by prefecture. The mean and median hardness of tap water in Japan was 48.9 ± 25.8 (1σ SD) and 46.0 mg/L, respectively. Compared with 27 other countries, Japan exhibited soft water with low-mineral content. Water hardness tended to be high in the Kanto region and low in the Hokkaido and Tohoku regions. The impact of the distribution system’s water pipes on tap water hardness is discussed using a unified index to evaluate variations in hardness from raw to tap water. A comparison of the variations in hardness showed that hardness variations from raw to purified water and from purified to tap water exhibited a 20% variation range. Furthermore, tap water hardness and its fluctuations in any region of Japan were found to be caused by raw water hardness. It was demonstrated that the distribution pipe system had no large impacts on water hardness.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3245
Author(s):  
Takahiro Takamatsu ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Tosiyuki Nakaegawa ◽  
Yuki Honda ◽  
...  

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.


2021 ◽  
Vol 26 (6) ◽  
pp. 6_53-6_55
Author(s):  
Kaoru ENDO
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